{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#                                           numpy 练习题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy 的array操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.导入numpy库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.建立一个一维数组 a 初始化为[4,5,6], (1)输出a 的类型（type）(2)输出a的各维度的大小（shape）(3)输出 a的第一个元素（值为4）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n",
      "(3,)\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "a = numpy.array([4,5,6])\n",
    "print(a.dtype)\n",
    "print(a.shape)\n",
    "print(a[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.建立一个二维数组 b,初始化为 [ [4, 5, 6],[1, 2, 3]] (1)输出各维度的大小（shape）(2)输出 b(0,0)，b(0,1),b(1,1) 这三个元素（对应值分别为4,5,2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "4 5 2\n"
     ]
    }
   ],
   "source": [
    "b = numpy.array([\n",
    "    [4,5,6],\n",
    "    [1,2,3]\n",
    "    ])\n",
    "print(b.shape)\n",
    "print(b[0][0],b[0][1],b[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.  (1)建立一个全0矩阵 a, 大小为 3x3; 类型为整型（提示: dtype = int）(2)建立一个全1矩阵b,大小为4x5;  (3)建立一个单位矩阵c ,大小为4x4; (4)生成一个随机数矩阵d,大小为 3x2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = numpy.zeros((3,3),dtype = int)\n",
    "b = numpy.ones((4,5))\n",
    "c = numpy.eye(4)\n",
    "d = numpy.random.random((3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. 建立一个数组 a,(值为[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] ) ,(1)打印a; (2)输出  下标为(2,3),(0,0) 这两个数组元素的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "12 1\n"
     ]
    }
   ],
   "source": [
    "a = numpy.array([\n",
    "    [1,2,3,4],\n",
    "    [5,6,7,8],\n",
    "    [9,10,11,12]\n",
    "])\n",
    "print(a)\n",
    "print(a[2][3],a[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.把上一题的 a数组的 0到1行 2到3列，放到b里面去，（此处不需要从新建立a,直接调用即可）(1),输出b;(2) 输出b 的（0,0）这个元素的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3 4]\n",
      " [7 8]]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "b = a[0:2,2:4]\n",
    "print(b)\n",
    "print(b[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### 7. 把第5题中数组a的最后两行所有元素放到 c中，（提示： a[1:2, :]）(1)输出 c ; (2) 输出 c 中第一行的最后一个元素（提示，使用 -1                 表示最后一个元素）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "c = a[1:3,:]\n",
    "print(c)\n",
    "print(c[0,-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8.建立数组a,初始化a为[[1, 2], [3, 4], [5, 6]]，输出 （0,0）（1,1）（2,0）这三个元素（提示： 使用 print(a[[0, 1, 2], [0, 1, 0]]) ）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 4 5]\n"
     ]
    }
   ],
   "source": [
    "a = numpy.array([\n",
    "    [1,2],\n",
    "    [3,4],\n",
    "    [5,6]\n",
    "])\n",
    "print(a[[0,1,2],[0,1,0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9.建立矩阵a ,初始化为[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]，输出(0,0),(1,2),(2,0),(3,1) (提示使用 b = np.array([0, 2, 0, 1])                     print(a[np.arange(4), b]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  6  7 11]\n"
     ]
    }
   ],
   "source": [
    "a = numpy.array([\n",
    "    [1,2,3],\n",
    "    [4,5,6],\n",
    "    [7,8,9],\n",
    "    [10,11,12]\n",
    "])\n",
    "b = numpy.array([0,2,0,1])\n",
    "print(a[numpy.arange(4),b])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10.对9 中输出的那四个元素，每个都加上10，然后重新输出矩阵a.(提示： a[np.arange(4), b] += 10 ）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  2  3]\n",
      " [ 4  5 16]\n",
      " [17  8  9]\n",
      " [10 21 12]]\n"
     ]
    }
   ],
   "source": [
    "a[numpy.arange(4),b] += 10\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### array 的数学运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11.  执行 x = np.array([1, 2])，然后输出 x 的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "x = numpy.array([1,2])\n",
    "print(x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12.执行 x = np.array([1.0, 2.0]) ，然后输出 x 的数据类类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "x = numpy.array([1.0,2.0])\n",
    "print(x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 13.执行 x = np.array([[1, 2], [3, 4]], dtype=np.float64) ，y = np.array([[5, 6], [7, 8]], dtype=np.float64)，然后输出 x+y ,和 np.add(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n"
     ]
    }
   ],
   "source": [
    "x = numpy.array([\n",
    "    [1,2],\n",
    "    [3,4]\n",
    "], dtype = numpy.float64)\n",
    "y = numpy.array([\n",
    "    [5,6],\n",
    "    [7,8]\n",
    "], dtype = numpy.float64)\n",
    "print(x + y)\n",
    "print(numpy.add(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 14. 利用 13题目中的x,y 输出 x-y 和 np.subtract(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n"
     ]
    }
   ],
   "source": [
    "print(x-y)\n",
    "print(numpy.subtract(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 15. 利用13题目中的x，y 输出 x*y ,和 np.multiply(x, y) 还有  np.dot(x,y),比较差异。然后自己换一个不是方阵的试试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      "[[19. 22.]\n",
      " [43. 50.]]\n",
      "[[ 5. 10.]\n",
      " [18. 24.]]\n",
      "[[ 5. 10.]\n",
      " [18. 24.]]\n",
      "[[17.]\n",
      " [39.]]\n"
     ]
    }
   ],
   "source": [
    "print(x * y)\n",
    "print(numpy.multiply(x,y))\n",
    "print(numpy.dot(x,y))\n",
    "z = numpy.array([[5],[6]], dtype = numpy.float64)\n",
    "print(x * z)\n",
    "print(numpy.multiply(x,z))\n",
    "print(numpy.dot(x,z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 16. 利用13题目中的x,y,输出 x / y .(提示 ： 使用函数 np.divide())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.divide(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 17. 利用13题目中的x,输出 x的 开方。(提示： 使用函数 np.sqrt() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.sqrt(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 18.利用13题目中的x,y ,执行 print(x.dot(y)) 和 print(np.dot(x,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19. 22.]\n",
      " [43. 50.]]\n",
      "[[19. 22.]\n",
      " [43. 50.]]\n"
     ]
    }
   ],
   "source": [
    "print(x.dot(y))\n",
    "print(numpy.dot(x,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 19.利用13题目中的 x,进行求和。提示：输出三种求和 (1)print(np.sum(x)):   (2)print(np.sum(x，axis =0 ));   (3)print(np.sum(x,axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "[4. 6.]\n",
      "[3. 7.]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.sum(x))\n",
    "print(numpy.sum(x, axis = 0))\n",
    "print(numpy.sum(x, axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 20.利用13题目中的 x,进行求平均数（提示：输出三种平均数(1)print(np.mean(x)) (2)print(np.mean(x,axis = 0))(3) print(np.mean(x,axis =1))）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5\n",
      "[2. 3.]\n",
      "[1.5 3.5]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.mean(x))\n",
    "print(numpy.mean(x, axis = 0))\n",
    "print(numpy.mean(x, axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 21.利用13题目中的x，对x 进行矩阵转置，然后输出转置后的结果，（提示： x.T 表示对 x 的转置）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 3.]\n",
      " [2. 4.]]\n"
     ]
    }
   ],
   "source": [
    "print(x.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 22.利用13题目中的x,求e的指数（提示： 函数 np.exp()）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.71828183  7.3890561 ]\n",
      " [20.08553692 54.59815003]]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.exp(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 23.利用13题目中的 x,求值最大的下标（提示(1)print(np.argmax(x)) ,(2) print(np.argmax(x, axis =0))(3)print(np.argmax(x),axis =1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "[1 1]\n",
      "[1 1]\n"
     ]
    }
   ],
   "source": [
    "print(numpy.argmax(x))\n",
    "print(numpy.argmax(x, axis = 0))\n",
    "print(numpy.argmax(x, axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 24,画图，y=x*x 其中 x = np.arange(0, 100, 0.1) （提示这里用到  matplotlib.pyplot 库）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x = numpy.arange(0,100,0.1)\n",
    "y = x*x\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 25.画图。画正弦函数和余弦函数， x = np.arange(0, 3 * np.pi, 0.1)(提示：这里用到 np.sin() np.cos() 函数和 matplotlib.pyplot 库)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = numpy.arange(0, 3 * numpy.pi, 0.1)\n",
    "y = numpy.sin(x)\n",
    "z = numpy.cos(x)\n",
    "plt.plot(x,y)\n",
    "plt.plot(x,z)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
